1. Field of the Invention
The invention relates to radiation therapy planning for the treatment of tumors or for stereotactic radiosurgery and more particularly to assist in the selection of optimum beam orientations for that therapy.
2. Description of Prior Art
The goal of radiation therapy is the accurate delivery of a radiation dose to tumors or other abnormal tissues in the human body to cause their obliteration, while keeping the dose received by sensitive organs and/or normal tissues to a tolerable minimum. Four main methods of radiation delivery are in use at the present time: Conformal, Intensity Modulated, Dynamic Arc and Tomotherapy. The Conformal method uses a relatively small number of radiation beams, each having a perimeter that conforms to the external shape of the abnormal tissues, as seen from the direction of the incoming beam. In Intensity Modulated Radiation Treatment (IMRT), each of the conformal beams is broken into a large number of pencil beams, or “beamlets”, and the dose delivered by each beamlet is controlled independently. In Dynamic Arc therapy, radiation is delivered by a source that travels continuously along one or more arcs partially surrounding the patient, with the shape of the radiation field changing continuously as needed to remain conformal to the perimeter of the abnormal region. In Tomotherapy, a “fan beam” of radiation rotates about the patient with the width of the fan controlled to conform to the shape of the abnormal tissues as it progresses, not only around the patient, but traveling along his/her length. In all cases, the sum of the doses delivered to each voxel of the abnormal tissues by all beams or beamlets is expected to be close to a prescribed dose, while the total dose delivered to voxels in sensitive organs and/or normal tissues has to be kept within some acceptable prescribed limits.
In the four methods indicated above, the abnormal tissues are included in a Planning Target Volume (PTV), located usually at the center of rotation of a radiation delivery system. With that approach, radiation coming from all the chosen orientations will always add up to a maximum dose in the PTV, while the dose to Organs-at-Risk (OARs) and/or normal tissues in the periphery can be lower.
Except in the case of Tomotherapy, where radiation always impinges on the PTV from all the angles around a circle, the selection of beam angles is of principal importance in radiation therapy. The experience of the Radiation Oncologist has traditionally been a most important factor in the successful planning of therapy for any particular clinical case. As will be described below, however, substantial efforts have been made to automate the selection of beam orientation angles to avoid a time-consuming “trial-and-error” process that is often required when a new location and/or shape of abnormality has to be treated. It is in this step of beam orientation angle selection that the present invention is expected to be of substantial assistance to the Radiation Oncologist.
Once a set of beam orientation angles has been selected, the next step is an “optimization” phase: The individual fluences to be delivered by each of the defined beams, beamlets or arcs, the so-called “beam weights”, have to be determined. In the case of IMRT and Tomotherapy, the determination of the optimum beamlet weights may require the solution of a set of a few thousand inconsistent simultaneous equations, usually with inequalities. Methods for the successful solution of the optimization problem have been found and are in use in a number of commercial software packages and in research institutions. For the case of IMRT, a comparison of one example each of the three most commonly used optimization methodologies is given in J. Llacer, et al, “Comparative Behaviour of the Dynamically Penalized Likelihood algorithm in inverse radiation therapy planning”, Physics in Medicine and Biology, Vol. 46, (2001), pp. 2637–2663.
We must now return to the previous step of determining the optimal beam orientations. As indicated above, a substantial scientific effort has been placed on finding suitable methods for such determination, prior to beam weight optimization. A brief description of those efforts will now be outlined. With some exceptions to be noted, all the methods have resorted to simplified optimizations and their comparison by a variety of measures, with or without some evolutionary means to improve the results as the selection proceeds towards a final solution. In general, the methods shown yield better optimizations than some reference set of beams, although they are very expensive in computer time and have not found sufficient favor in clinical applications.
S. Söderstrom and A. Brahme in “Selection of suitable beam orientations in radiation therapy using entropy and Fourier transform measures”, Physics in Medicine and Biology, Vol. 37, (1992), pp. 911–924, use entropy and also the integral of the low frequency part of the Fourier transform of optimum “beam profiles” (a profile of contiguous beam weights) as measures of the information content in those profiles and the amount of gross beam structure, respectively. T. Bortfeld and W. Schlegel in “Optimization of beam orientations in radiation therapy: some theoretical considerations”, Physics in Medicine and Biology, Vol. 38, (1993), pp. 291–304, use simulated annealing in frequency domain and arrive at the conclusion that, for multiple-beam irradiation (more than three beams) the optimum beam configuration tends to be an even distribution over an angular range of 0 to 2π. P. Gokhale, et al, in “Determination of beam orientation in radiotherapy planning”, Medical Physics, Vol. 21 (3), (1994), pp. 393–400 use mathematical techniques to determine the “path of least resistance” from the PTV to the patient surface and select the beams with lowest resistance. S. Webb in “Optimizing the planning of intensity-modulated radiotherapy”, Physics in Medicine and Biology, Vol. 39, (1994), pp. 2229–2246, arrives at the conclusion that the use of 7 to 9 uniformly spaced beam directions can lead to acceptable optimizations, except when the PTV and OARs overlap. He provides a comment, but no solution for, the problem of optimizing field orientation. S. M. Morrill, et al, in “Conventional treatment planning optimization using simulated annealing”, Int. J. Radiation Oncology Biol. Phys., Vol 32, Supplement 1, (1994), p. 298, describes the reduction of needed beam angles to a relatively small number by simulated annealing as the optimization phase proceeds. S. Söderstrom and A. Brahme in “Which is the most suitable number of photon beam portals in coplanar radiation therapy?, Int. J. Radiation Oncology Biol. Phys., Vol 33, No. 1, (1995), pp. 151–159 reach the conclusion that with non-uniform beams (IMRT), the level of complication-free tumor control is adequate with 3 beams to 5 beams and more beams do not result in substantial improvements. D. L. McSchan, et al, in “Advanced interactive planning techniques for conformal therapy: high level beam descriptions and volumetric mapping techniques”, Int. J. Radiation Oncology Biol. Phys., Vol 33, No. 5, (1995), pp. 1061–1072, describes the use of “Beam's Eye Volumetrics”, a graphical interactive method to define a PTV and OARs in terms of arcs, cones and other shapes. Those shapes can then be used to aid in the selection of beams for conformal therapy.
C. X. Yu in “Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy”, Physics in Medicine and Biology, Vol. 40 (1995) pp. 1435–1449 and U.S. Pat. No. 5,818,902 “Intensity modulated arc therapy with dynamic multi-leaf collimation”, (Oct. 6, 1998) describe a “hybrid” between Tomotherapy and Dynamic Arc therapy in which a gantry stops at a number of selected angles around the patient. Tomotherapy is described in T. R. Mackie, et al, “Tomotherapy: A new concept for the delivery of dynamic conformal radiotherapy”, Medical Physics, Vol 20 (6), (1993), pp. 1709–1719.
G. A. Ezzell in “Genetic and geometric optimization of three-dimensional radiation therapy treatment planning”, Medical Physics, Vol. 23 (3) (1996) pp. 293–305, describes what appears to be the first application of genetic algorithms to optimization. Together with geometric considerations for the elimination of “undesirable” beamlets, it reaches optimization angles and weights that lead to reasonable optimizations. M. Langer, et al, in “A generic genetic algorithm for generating beam weights”, Medical Physics, Vol. 23 (6), (1996), pp. 965–971, describe the use of a genetic algorithm for the optimization of up to 36 beams (or beamlets) for the treatment of abdominal tumors.
V. C. Ling, et al, in “Conformal radiation treatment of prostate cancer using inversely-planned intensity-modulated photon beams produced with dynamic multileaf collimation”, Int. J. Radiation Oncology Biol. Phys., Vol. 35, No. 4, (1996), pp. 721–730 report promising results by the use of six co-planar beams for the treatment of prostate cancer. Those beams are pair-wise opposed at −45°, 0° and +45° with respect to the horizontally placed patient. S. K. Das and L. B. Marks, in “Selection of coplanar or noncoplanar beams using three-dimensional optimization based on maximum beam separation and minimized nontarget irradiation”, Int. J. Radiation Oncology Biol. Phys., Vol. 38, No. 3, (1997), pp. 643–655 report on the use of two measures for the selection of beam angles: maximum beam separation for steep dose gradient at target edge and a length function related to normal tissue traversed by a beam. By a suitable combination of those measures, reasonable sets of beam angles are found for clinical situations. H-M. Lu, et al, in “Optimized beam planning for linear accelerator-based stereotactic radiosurgery”, Int. J. Radiation Oncology Biol. Phys., Vol. 39, No. 5, (1997), pp. 1183–1189, use a Beam's Eye View of possible beam arc combinations and phase-space scrutiny to reduce the available angles. Finally, a simulated annealing method yields the optimal beam angles.
O. C. L. Haas, et al, in “Optimization of beam orientation in radiotherapy using planar geometry”, Physics in Medicine and Biology, Vol. 43 (1998), pp. 2179–2193, returns to the use of a genetic algorithm specifically for coplanar beams, using a methodology that allows the algorithm to search in parallel for different objectives, resulting in a speed up of the search. B. C. J. Cho, et al, in “The development of target-eye-view maps for selection of coplanar or noncoplanar beams in conformal radiotherapy treatment planning”, Medical Physics, Vol. 26, (11), (1999) pp. 2367–2372, use Beam's Eye View (BEV) techniques together with a Target's Eye View (TEV) to map the OARs and check for regions of overlap with the PTV in a Mercator spherical map, thus assisting visually in a computer screen to select beam orientations that present minimum conflicts. C. G. Rowbottom, et al, in “Constrained customization of non-coplanar beam orientations in radiotherapy of brain tumors”, Physics in Medicine and Biology, Vol. 44 (1999) pp. 383–399, used a methodology consisting of single and multibeam cost functions, together with simulated annealing to produce well-spaced, customized beam orientations for individual patients.
M. E. Hosseini-Ashrafe, et al, in “Pre-optimization of radiotherapy treatment planning: an artificial neural network classification aided technique”, Physics in Medicine and Biology, Vol. 44 (1999) pp. 1513–1528, introduce artificial neural networks to the problem of beam selection, an approach that is also followed by C. G. Rowbottom, et al, in “Beam orientation customization using an artificial neural network”, Physics in Medicine and Biology, Vol. 44 (1999) pp. 2251–2262. The initial use of neural networks for the beam selection problem has not been followed in later years.
M. Braunstein and R. Y. Levine, in “Optimum beam configurations in tomographic intensity modulated radiation therapy”, Physics in Medicine and Biology, No. 45 (2000), pp. 305–328, have tested a method of ranking particular beam orientations in terms of the contributed dose to the PTV and OARs. For simple simulated clinical cases, they find that there are, indeed, preferred beam directions. O. Jäkel and J. Debus, in “Selection of beam angles for radiotherapy of skull base tumours using charged particles”, in Physics in Medicine and Biology, Vol. 45 (2000) pp. 1229–1241, analyze the beams selected in 50 patients treated by conformal therapy with X-rays and arrive at a consensus of which beam orientations should then be used for treatments with charged particles.
The next group includes a number of papers and a Patent by A. B. Pugachev and co-workers at Stanford University. Their work follows a progression towards a final method that could be applied in the clinic. It is characterized initially by simplified optimizations and later by progressively simpler and better search methods based on the geometry of the PTV and OARs. A. B. Pugachev, et al, “Beam orientation optimization in intensity-modulated radiation treatment planning”, Medical Physics, Vol. 27 (6) (2000), pp. 1238–1245; A. V. Pugachev, et al, “Role of beam orientation optimization in intensity-modulated radiation therapy”, Int. J. Radiation Oncology Biol. Phys. Vol. 50, No. 2 (2001), pp. 551–560; A. Pugachev and L. Xing, “Pseudo beam's-eye-view as applied to beam orientation selection in intensity-modulated radiation therapy”, Int. J. Radiation Oncology Biol. Phys. Vol. 51, No. 5 (2001), pp. 1361–1370; A. Pugachev and L. Xing, “Computer-assisted selection of coplanar beam orientations in intensity-modulated radiation therapy”, Physics in Medicine and Biology, Vol. 46, (2001), pp. 2467–2476 and U.S. Pat. No. 6,504,899, “Method for selecting beam orientations in intensity modulated radiation therapy”, (Jan. 7, 2003); A. Pugachev and L. Xing, “Incorporating prior knowledge into beam orientation optimization in IMRT”, ”, Int. J. Radiation Oncology Biol. Phys. Vol. 54, No. 5 (2002), pp. 1565–1574.
E. Woudstra and P R M Storchi, in “Constrained treatment planning using sequential beam selection”, Physics in Medicine and Biology, Vol. 45 (2000), pp. 2133–2149, have developed a method based on pre-calculated dose distributions for all candidate orientations and a sequential method to add beams into the selection by a scoring method. C. G. Rowbottom, et al, in “Simultaneous optimization of beam orientations and beam weights in conformal therapy” Medical Physics Vol. 28 (8) (2001) pp. 1696–1702, have developed an optimization algorithm for both beam orientations and beam weights based on a random sampling of the PTV and OAR voxels which produce acceptable dose distributions and may be able to improve on those obtained by human planners. In T. Y. Yang, et al, U.S. Pat. No. 6,260,005, “Falcon: Automated optimization method for arbitrary assessment criteria”, (Jul. 10, 2001) a complete optimization methodology is described that is applied to radiation therapy as an example. The orientation of the therapy beams is not generated by the method, but has to be given by the user.
Genetic algorithms for beam orientation optimization have reappeared: X. Wu and Y. Zhu in “A mixed-encoding genetic algorithm with beam constraint for conformal radiotherapy treatment planning”, Medical Physics, Vol. 27 (11) (2000) pp. 2508–2516 propose a new hierarchical algorithm that combines binary and floating-point encoding to both select beam directions and beam weights. P. Zhang, et al, in “Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing”, Medical Physics, Vol. 28 (8) (2001) pp. 1746–1752 use a guided evolutionary simulated annealing method that yields results in a more conformal plan and reduces treatment administration time, in comparison with manual methods. Y. Li, et al, in “Automatic beam angle selection in IMRT planning using genetic algorithm”, Physics in Medicine and Biology, Vol. 49 (2004), pp. 1915–1932, separate the genetic selection part of the problem from the optimization and can carry out both in reasonable times, yielding sensible results that may be clinically useful.
D. Djajaputra, et al, in “Algorithm and performance of a clinical IMRT beam-angle optimization system”, Physics in Medicine and Biology, Vol. 48 (2003) pp. 3191–3212 have accelerated the processes of dose calculation, optimization and beam selection by simplification and the use of pre-computed dose kernels. They still report many hours of computation for relatively complex therapy cases. Finally, F. J. Bova in U.S. Pat. No. 6,661,872, “Intensity modulated radiation therapy planning system”, (Dec. 9, 2003) describes a method for beam selection that relies on replacing arcs, as in Dynamic Arc therapy, with a number of equally spaced source positions along the arcs and then using a scoring system to define which positions are to be kept in a final beam selection.
It is recognized by workers in the area of beam orientation optimization that the problem is a very difficult one to solve in a satisfactory manner within a computation time that is useful in the clinic. Of all the work reviewed above, only the object of U.S. Pat. No. 6,504,899, issued Jan. 7, 2003 to Pugachev and Xing, appears to have a chance of being used extensively in the foreseeable future for beam selection in the clinic. Their method does indeed select beam orientations that are reasonable, within the time constraints imposed by clinical practice. The method of Pugachev and Xing, however, suffers from the severe limitation that is only applicable to therapy with coplanar beams, that is, beams that originate in a radiation source that can move along the perimeter of a circle. In the frequent cases of therapy for tumors or other abnormalities in the head and/or into the neck area, therapy beams can be delivered from non-coplanar beams, that is from a source that can move on the surface of a sphere, except for positional conflicts with the neck. The ability to select such non-coplanar beams is a necessity in order to achieve treatment plans that yield the desired high dose to the PTV and provide adequate sparing of the very sensitive organs and healthy tissues in the head, for example. The extension of Pugachev and Xing's method to non-coplanar beams is prohibitive, as would be the case with all the other selection methods reviewed above. The extremely high increase in the number of possible candidate beam orientations in going from a circular set of source positions to a spherical one results in an inordinate increase in computation time.
The main cause of difficulty with all the attempts at solving the beam selection problem is the overly ambitious goal that has been set, that of selecting automatically all the beams that should be used in the optimization of a particular clinical case. What has been overlooked is that an appropriate computational analysis of the geometry of the PTV and OARs provides a wealth of information that can be used to select several very important coplanar or non-coplanar beam orientations in a number of difficult clinical cases, particularly those in which one or more OARs are adjacent to the PTV. The use of beam's or target's eye views and projections of the PTV and OARs onto spherical or cylindrical surfaces, as in some of the work reviewed above, are steps in the desired direction and such methods have been incorporated extensively in commercial treatment planning software packages. Those methods, however, do not use make good use of the capabilities of current computational techniques in Computer Vision, which can provide more accurate and complete information about the nature of the boundaries between a PTV and any adjacent OARs. Such information will be shown to allow the selection of beam orientations yielding optimizations that may be limited only by fundamental radiation physics.